Instructions to use trollek/Qwen2-0.5B-DiffusionPrompter-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trollek/Qwen2-0.5B-DiffusionPrompter-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="trollek/Qwen2-0.5B-DiffusionPrompter-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trollek/Qwen2-0.5B-DiffusionPrompter-v0.1")
model = AutoModelForCausalLM.from_pretrained("trollek/Qwen2-0.5B-DiffusionPrompter-v0.1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trollek/Qwen2-0.5B-DiffusionPrompter-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trollek/Qwen2-0.5B-DiffusionPrompter-v0.1

SGLang

How to use trollek/Qwen2-0.5B-DiffusionPrompter-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trollek/Qwen2-0.5B-DiffusionPrompter-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use trollek/Qwen2-0.5B-DiffusionPrompter-v0.1 with Docker Model Runner:
```
docker model run hf.co/trollek/Qwen2-0.5B-DiffusionPrompter-v0.1
```

Can you teach me how to organize the dataset?你能教我怎么组织数据集吗？

by sherlockbt - opened Jul 17, 2024

Discussion

sherlockbt

Jul 17, 2024

你能指导我一下怎么把你提及的数据集转换成训练的数据格式的，我想复现你的训练结果
Can you guide me how to convert the dataset you mentioned into the training data format? I want to reproduce your training results.

trollek

Owner Jul 17, 2024

I can! I did it in a few stages. Fumbling in darkness at the time. Here are the config files chronologically:

### model
model_name_or_path: Qwen/Qwen2-0.5B

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
loraplus_lr_ratio: 16.0
lora_rank: 8
lora_alpha: 16
use_unsloth: true
seed: 3141

### dataset
dataset: ninja_diffusion
template: hermes_chatml (ChatML without system)
cutoff_len: 1024
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: qwen2/0.5B/sd-prompter
logging_steps: 1
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 8
gradient_accumulation_steps: 4
learning_rate: 0.0001
num_train_epochs: 4.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
flash_attn: fa2

### eval
val_size: 0.02
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 200

### model
model_name_or_path: qwen2/0.5B/sd-prompter

### method
stage: pt
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 1
badam_start_block: 10
seed: 31415

### dataset
dataset: stable_diffusion_pt (Gustavosta/Stable-Diffusion-Prompts)
cutoff_len: 1024
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: qwen2/0.5B/SD-base
logging_steps: 5
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.00001
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.01
pure_bf16: true
flash_attn: fa2

### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000

### model
model_name_or_path:  qwen2/0.5B/SD-base

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
use_dora: true
lora_rank: 16
lora_alpha: 16
use_unsloth: true
seed: 3141
additional_target: lm_head,embed_tokens

### dataset
dataset: ninja_diffusion,sd_concepts (the sd-concepts dataset is a json file in this repo)
template: hermes_chatml
cutoff_len: 1024
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: qwen2/0.5B/Qwen2-DiffusionPrompter-v0.1
logging_steps: 1
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 4
gradient_accumulation_steps: 8
learning_rate: 0.00005
num_train_epochs: 2
lr_scheduler_type: constant_with_warmup
warmup_ratio: 0.01
bf16: true
flash_attn: fa2

### eval
val_size: 0.05
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 100

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment